# BoostClean: Automated Error Detection and Repair for Machine Learning

arXiv: https://arxiv.org/abs/1711.01299 · HTML: https://mlsanity.com/paper/1711.01299/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2017.

## Built on by (most cited first, 23 of 23)

- [Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments](https://mlsanity.com/paper/2206.10013/) (2022)
- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
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- [Data Cleaning and Machine Learning: A Systematic Literature Review](https://mlsanity.com/paper/2310.01765/) (2023)
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- [Naive Bayes Classifiers over Missing Data: Decision and Poisoning](https://mlsanity.com/paper/2303.04811/) (2023)
- [DataAssist: A Machine Learning Approach to Data Cleaning and Preparation](https://mlsanity.com/paper/2307.07119/) (2023)
- [Certain and Approximately Certain Models for Statistical Learning](https://mlsanity.com/paper/2402.17926/) (2024)
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- [Stress-Testing ML Pipelines with Adversarial Data Corruption](https://mlsanity.com/paper/2506.01230/) (2025)
- [Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion Detection](https://mlsanity.com/paper/2105.10041/) (2021)
- [Man versus Machine: AutoML and Human Experts' Role in Phishing Detection](https://mlsanity.com/paper/2108.12193/) (2021)
- [Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI](https://mlsanity.com/paper/2207.09109/) (2022)
- [AutoCure: Automated Tabular Data Curation Technique for ML Pipelines](https://mlsanity.com/paper/2304.13636/) (2023)
- [RAG-Driven Data Quality Governance for Enterprise ERP Systems](https://mlsanity.com/paper/2511.16700/) (2025)
- [Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models](https://mlsanity.com/paper/2607.19847/) (2026)

## Abstract (arXiv)

Predictive models based on machine learning can be highly sensitive to data error. Training data are often combined with a variety of different sources, each susceptible to different types of inconsistencies, and new data streams during prediction time, the model may encounter previously unseen inconsistencies. An important class of such inconsistencies is domain value violations that occur when an attribute value is outside of an allowed domain. We explore automatically detecting and repairing such violations by leveraging the often available clean test labels to determine whether a given detection and repair combination will improve model accuracy. We present BoostClean which automatically selects an ensemble of error detection and repair combinations using statistical boosting. BoostClean selects this ensemble from an extensible library that is pre-populated general detection functions, including a novel detector based on the Word2Vec deep learning model, which detects errors across a diverse set of domains. Our evaluation on a collection of 12 datasets from Kaggle, the UCI repository, real-world data analyses, and production datasets that show that Boost- Clean can increase absolute prediction accuracy by up to 9% over the best non-ensembled alternatives. Our optimizations including parallelism, materialization, and indexing techniques show a 22.2x end-to-end speedup on a 16-core machine.